Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1637874750
F. Alsaqre
{"title":"AN IMPROVED FRACTIONAL TWO-DIMENSIONAL PRINCIPAL COMPONENT ANALYSIS FOR FACE RECOGNITION","authors":"F. Alsaqre","doi":"10.5455/jjcit.71-1637874750","DOIUrl":"https://doi.org/10.5455/jjcit.71-1637874750","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1640595124
Ichrak Khoulqi, N. Idrissi
{"title":"CERVICAL CANCER DETECTION AND CLASSIFICATION USING MRIS","authors":"Ichrak Khoulqi, N. Idrissi","doi":"10.5455/jjcit.71-1640595124","DOIUrl":"https://doi.org/10.5455/jjcit.71-1640595124","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"17 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1638972901
Rabah Mazouzi, M. Ndenga, Cyril de Runz, H. Akdag
{"title":"Consensual based classification as emergent decisions in a complex system","authors":"Rabah Mazouzi, M. Ndenga, Cyril de Runz, H. Akdag","doi":"10.5455/jjcit.71-1638972901","DOIUrl":"https://doi.org/10.5455/jjcit.71-1638972901","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1641418357
S. Benhamdi, A. Babouri, Raja Chiky, Jamal Nebhen
{"title":"RULE-BASED APPROACH FOR CONTEXT-AWARE COLLABORATIVE RECOMMENDER SYSTEM","authors":"S. Benhamdi, A. Babouri, Raja Chiky, Jamal Nebhen","doi":"10.5455/jjcit.71-1641418357","DOIUrl":"https://doi.org/10.5455/jjcit.71-1641418357","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1655376900
P. N, Prashantha J
In this paper, we propose an efficient fusion framework for brain magnetic resonance (MR) image classification using deep learning and handcrafted feature extraction methods, namely histogram of oriented gradients (HOG) and local binary patterns (LBP). The proposed framework aims to: (1) determine the optimal handcrafted features by Genetic Algorithm (GA) (2) discover the fully connected (FC) layers features using fine-tuned convolutional neural network (CNN) (3) employs the canonical correlation analysis (CCA) and the discriminant correlation analysis (DCA) methods in feature level fusion. Extensive experiments were conducted and demonstrated the classification performance on three benchmark datasets, viz., RD-DB1, TCIA-IXI-DB2 and TWB-HM-DB3. The mean accuracy of 68.69%, 90.35%, and 93.15% from CCA and 77.22%, 100.00%, and 99.40% from DCA was achieved by the Support Vector Machines (SVM) sigmoid kernel classifier on RD-DB1, TCIA-IXI-DB2, and TWB-HM-DB3 respectively. The obtained results of the proposed framework outperform when compared with other state-of-art works.
本文提出了一种基于深度学习和手工特征提取方法的脑磁共振图像分类融合框架，即定向梯度直方图(HOG)和局部二值模式(LBP)。该框架旨在:(1)通过遗传算法(GA)确定最优的手工特征;(2)使用微调卷积神经网络(CNN)发现全连接(FC)层特征;(3)在特征级融合中采用典型相关分析(CCA)和判别相关分析(DCA)方法。在RD-DB1、tcia - ix - db2和TWB-HM-DB3三个基准数据集上进行了大量实验，验证了分类性能。支持向量机(SVM) sigmoid核分类器在RD-DB1、TCIA-IXI-DB2和TWB-HM-DB3上对CCA的平均准确率分别为68.69%、90.35%和93.15%，对DCA的平均准确率分别为77.22%、100.00%和99.40%。与其他最先进的工作相比，所提出的框架获得的结果优于其他最先进的工作。
{"title":"FEATURE LEVEL FUSION FRAMEWORK FOR BRAIN MR IMAGE CLASSIFICATION USING SUPERVISED DEEP LEARNING AND HAND CRAFTED FEATURES","authors":"P. N, Prashantha J","doi":"10.5455/jjcit.71-1655376900","DOIUrl":"https://doi.org/10.5455/jjcit.71-1655376900","url":null,"abstract":"In this paper, we propose an efficient fusion framework for brain magnetic resonance (MR) image classification using deep learning and handcrafted feature extraction methods, namely histogram of oriented gradients (HOG) and local binary patterns (LBP). The proposed framework aims to: (1) determine the optimal handcrafted features by Genetic Algorithm (GA) (2) discover the fully connected (FC) layers features using fine-tuned convolutional neural network (CNN) (3) employs the canonical correlation analysis (CCA) and the discriminant correlation analysis (DCA) methods in feature level fusion. Extensive experiments were conducted and demonstrated the classification performance on three benchmark datasets, viz., RD-DB1, TCIA-IXI-DB2 and TWB-HM-DB3. The mean accuracy of 68.69%, 90.35%, and 93.15% from CCA and 77.22%, 100.00%, and 99.40% from DCA was achieved by the Support Vector Machines (SVM) sigmoid kernel classifier on RD-DB1, TCIA-IXI-DB2, and TWB-HM-DB3 respectively. The obtained results of the proposed framework outperform when compared with other state-of-art works.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1646912715
Nur Hayatin, Suraya Alias, L. Hung, M. Sainin
Sentiment analysis is the field in data science to achieve a broader holistic view of users’ needs and expectations. Indonesian user opinions have the potential to manage to be valuable information using sentiment analysis tasks. One of the most supervised learning techniques used in Indonesian sentiment analysis is the Naïve Bayes classifier. The classifier can be optimized and tuned in various models to increase the sentiment analysis model performance. This research aims to examine the performance of various Naïve Bayes models in sentiment analysis, especially when implemented in small datasets to handle overfitting problems. Four different Naïve Bayes models used are Gaussian, Multinomial, Complement, and Bernoulli. We also analyse the effect of various pre-processing techniques on the models’ performance. Moreover, we build the first fashion dataset from the Indonesian marketplace which has a unique character compared to the datasets from other domains. Finally, we also use the various dataset in the experiment to test the Naïve Bayes models' performance. From the experiment result, Complement Naïve Bayes is superior to other models, especially in handling overfitting with F1-score of approximately 0.82.
{"title":"SENTIMENT ANALYSIS BASED ON PROBABILISTIC CLASSIFIER TECHNIQUES IN VARIOUS INDONESIAN REVIEW DATA","authors":"Nur Hayatin, Suraya Alias, L. Hung, M. Sainin","doi":"10.5455/jjcit.71-1646912715","DOIUrl":"https://doi.org/10.5455/jjcit.71-1646912715","url":null,"abstract":"Sentiment analysis is the field in data science to achieve a broader holistic view of users’ needs and expectations. Indonesian user opinions have the potential to manage to be valuable information using sentiment analysis tasks. One of the most supervised learning techniques used in Indonesian sentiment analysis is the Naïve Bayes classifier. The classifier can be optimized and tuned in various models to increase the sentiment analysis model performance. This research aims to examine the performance of various Naïve Bayes models in sentiment analysis, especially when implemented in small datasets to handle overfitting problems. Four different Naïve Bayes models used are Gaussian, Multinomial, Complement, and Bernoulli. We also analyse the effect of various pre-processing techniques on the models’ performance. Moreover, we build the first fashion dataset from the Indonesian marketplace which has a unique character compared to the datasets from other domains. Finally, we also use the various dataset in the experiment to test the Naïve Bayes models' performance. From the experiment result, Complement Naïve Bayes is superior to other models, especially in handling overfitting with F1-score of approximately 0.82.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1639410312
Mohammed Alweshah
In the field of medicine, there is a need to filter data to find information that is relevant for specific research problems. However, in the realm of scientific study, the process of selecting the appropriate data or features is a substantial and challenging problem. Therefore, in this paper, two wrapper feature selection (FS) methods based on novel metaheuristic algorithms named the arithmetic optimization algorithm (AOA) and the great deluge algorithm (GDA) were used to attempt to tackle the medical diagnostics challenge. Two methods, AOA and AOA-GD were tested on 23 medical benchmark datasets. According to all of the experimental data, the hybridization of the GDA with the AOA considerably increased the AOA’s search capability. The AOA-GD method was then compared with two previous wrapper FS approaches;namely, the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC) and the binary moth flame optimization with Lévy flight (LBMFO_V3). When applied to the 23 medical benchmark datasets, the AOA-GD achieved an accuracy rate of 0.80, thereby surpassing both the CHIO-GC and LBMFO V3.
{"title":"Hybridization of Arithmetic Optimization with Great Deluge Algorithms for Feature Selection Problems in Medical Diagnoses","authors":"Mohammed Alweshah","doi":"10.5455/jjcit.71-1639410312","DOIUrl":"https://doi.org/10.5455/jjcit.71-1639410312","url":null,"abstract":"In the field of medicine, there is a need to filter data to find information that is relevant for specific research problems. However, in the realm of scientific study, the process of selecting the appropriate data or features is a substantial and challenging problem. Therefore, in this paper, two wrapper feature selection (FS) methods based on novel metaheuristic algorithms named the arithmetic optimization algorithm (AOA) and the great deluge algorithm (GDA) were used to attempt to tackle the medical diagnostics challenge. Two methods, AOA and AOA-GD were tested on 23 medical benchmark datasets. According to all of the experimental data, the hybridization of the GDA with the AOA considerably increased the AOA’s search capability. The AOA-GD method was then compared with two previous wrapper FS approaches;namely, the coronavirus herd immunity optimizer with greedy crossover operator (CHIO-GC) and the binary moth flame optimization with Lévy flight (LBMFO_V3). When applied to the 23 medical benchmark datasets, the AOA-GD achieved an accuracy rate of 0.80, thereby surpassing both the CHIO-GC and LBMFO V3.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1661423262
Remah Younisse, Mouhammd Alkasassbeh, Mohammad Almseidin, Hamza Abdi
The wild nature of humans has become civilized, and the weapons they use to attack each other are now digitized. Security over the Internet usually takes a defensive shape, aiming to fight against attacks created for malicious reasons. Invaders’ actions over the internet can take patterns by going through specific steps every time they attack. These patterns can be used to predict, mitigate and stop these attacks. This study proposes a method to label datasets related to multi-stage attacks according to attack stages rather than the attack type. These datasets can be used later in machine learning models to build intelligent defensive models. On the other hand, we propose a method to predict and early kill attacks in an active directory environment, such as Kerberoasting attacks. In this study, we have collected the data related to a suggested Kerberoasting attack scenario in pcap files. Every pcap file contains the data related to a particular stage of the attack lifecycle, the extracted information from the pcap files was used to highlight the features and specific activities during every stage. The information was used to draw an efficient defensive plan against the attack. Here we propose a methodology to draw equivalent defensive plans for other similar attacks as the Kerberoasting attack covered in this study.
{"title":"An Early Detection Model for Kerberoasting\u0000Attacks and Dataset Labeling","authors":"Remah Younisse, Mouhammd Alkasassbeh, Mohammad Almseidin, Hamza Abdi","doi":"10.5455/jjcit.71-1661423262","DOIUrl":"https://doi.org/10.5455/jjcit.71-1661423262","url":null,"abstract":"The wild nature of humans has become civilized, and the weapons they use to attack each other are now digitized. Security over the Internet usually takes a defensive shape, aiming to fight against attacks created for malicious reasons. Invaders’ actions over the internet can take patterns by going through specific steps every time they attack. These patterns can be used to predict, mitigate and stop these attacks. This study proposes a method to label datasets related to multi-stage attacks according to attack stages rather than the attack type. These datasets can be used later in machine learning models to build intelligent defensive models. On the other hand, we propose a method to predict and early kill attacks in an active directory environment, such as Kerberoasting attacks. In this study, we have collected the data related to a suggested Kerberoasting attack scenario in pcap files. Every pcap file contains the data related to a particular stage of the attack lifecycle, the extracted information from the pcap files was used to highlight the features and specific activities during every stage. The information was used to draw an efficient defensive plan against the attack. Here we propose a methodology to draw equivalent defensive plans for other similar attacks as the Kerberoasting attack covered in this study.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70821062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1632868199
Malek Barhoush, Bilal Alguni, Rafat Hammad, Mohammad Fawareh, Rana Hassan
{"title":"DES22: DES BASED ALGORITHM WITH IMPROVED SECURITY","authors":"Malek Barhoush, Bilal Alguni, Rafat Hammad, Mohammad Fawareh, Rana Hassan","doi":"10.5455/jjcit.71-1632868199","DOIUrl":"https://doi.org/10.5455/jjcit.71-1632868199","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5455/jjcit.71-1644147942
R. Khatri, D. Mishra
{"title":"FULLY OPTIMIZED ULTRA WIDEBAND RF RECEIVER FRONT END","authors":"R. Khatri, D. Mishra","doi":"10.5455/jjcit.71-1644147942","DOIUrl":"https://doi.org/10.5455/jjcit.71-1644147942","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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